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parameter-efficient fine-tuning

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.

Papers

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Showing 151–200 of 935 papers

TitleStatusHype
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African LanguagesCode1
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for MambaCode1
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative Perception with Parameter-Efficient Fine-TuningCode1
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical MapsCode1
MasakhaNEWS: News Topic Classification for African languagesCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Expanding Sparse Tuning for Low Memory UsageCode1
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-TuningCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
FedJudge: Federated Legal Large Language ModelCode1
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual taskCode1
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for EchocardiographyCode1
Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam TasksCode1
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion ModelsCode1
Empirical Study of PEFT techniques for Winter Wheat SegmentationCode1
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision TransformersCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Content-based Controls For Music Large Language ModelingCode1
DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated ImagesCode1
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design ApproachCode1
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical ApplicationsCode1
Advancing Parameter Efficiency in Fine-tuning via Representation EditingCode1
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Efficient Test Time Adapter Ensembling for Low-resource Language VarietiesCode1
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
Extending Whisper with prompt tuning to target-speaker ASRCode1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-TuningCode1
A Comprehensive Analysis of Adapter EfficiencyCode1
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank StructuresCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language ModelsCode1
Parameter Efficient Fine-tuning via Explained Variance AdaptationCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
MEFT: Memory-Efficient Fine-Tuning through Sparse AdapterCode1
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA RegionCode1
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and InferenceCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward PropagationCode1
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
LoRA Soups: Merging LoRAs for Practical Skill Composition TasksCode1
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All datasetsBoolQHellaSwagWinoGrande

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )82.63—Unverified
2LLaMA2-7bAccuracy (% )82.63—Unverified
3LLaMA2-7bAccuracy (% )81.93—Unverified
4LLaMA2-7bAccuracy (% )80.28—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )76.68—Unverified
2LLaMA2-7bAccuracy (% )76.67—Unverified
3LLaMA2-7bAccuracy (% )76.27—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )70.8—Unverified
2LLaMA2-7bAccuracy (% )70.09—Unverified
3LLaMA2-7bAccuracy (% )69.85—Unverified